Migratory Variation in Mackenzie River System Broad Whitefish: Insights from Otolith Strontium Distributions
Bibliographic record
Abstract
Abstract There is growing recognition of the global importance of preserving biodiversity. While many organisms show immense variation in intraspecific biodiversity, for example in life history variation and migratory strategies among conspecific populations, accurate descriptions of such variation are lacking for the majority of contemporary species. One such example is the broad whitefishCoregonus nasusof the lower Mackenzie River system in Canada's Northwest Territories, where anadromous, lacustrine, and putative riverine populations are thought to exist. In this study we resolve migratory variation exhibited by lower Mackenzie River broad whitefish by employing otolith microchemistry and find that (1) anadromous, lacustrine, and riverine populations exist in this system, (2) a high degree of variability exists within anadromous broad whitefish (e.g., varying degrees of marine and estuarine use), and (3) lacustrine populations are not composed solely of resident fish as anadromous broad whitefish occasionally migrate to, and stay in, lacustrine habitat. Overall, our results are consistent with the suggestion that there may be a higher level of migratory complexity in this system than previously reported and these results will be important in guiding the conservation of intraspecific biodiversity in Mackenzie River system broad whitefish.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".